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• Introduction
  • The concept of CLC Single Cell Analysis Module
  • Contact information
  • System requirements and installation
    • System requirements
    • Installation of modules
    • Uninstalling modules
  • Licensing modules
    • Server License
  • The QIAGEN Cell Ontology
  • Reference data management
    • The Reference Data Manager
  • Running tools and workflows
• Data import
  • Import Expression Matrix
  • Import Cell Annotations
  • Import Cell Clusters
  • Import Cell Clonotypes
• Data export
  • Export Cell Ranger HDF5 expression matrix
  • Export Loom expression matrix
  • Export MEX expression matrix
• Creating a count matrix from FASTQ
  • Annotate Reads with Cell and UMI
    • Setting the sample name
    • Interpreting the output of Annotate Reads with Cell and UMI
  • Single Cell RNA-Seq Analysis
    • The Single Cell RNA-Seq Analysis report
    • The Single Cell RNA-Seq Analysis algorithm
• Cell preparation
  • QC for Single Cell
    • Empty droplets filter
    • Count-based and extra-chromosomal filters
    • Doublets filter
    • Interpreting the output of QC for Single Cell
    • Choosing barcodes to retain
    • The cell calling algorithm
    • The doublet calling algorithm
  • Normalize Single Cell Data
    • When is batch correction appropriate?
    • Interpreting the output of Normalize Single Cell Data
    • The Normalize Single Cell Data algorithm
  • Combine Cell Annotations
  • Combine Cell Clusters
  • Convert Metadata to Cell Annotations
  • Update to Common Sample Name
• Noise reduction through feature selection and PCA
  • Feature selection and PCA
    • Calculation of estimated biological variation
• Cell Annotation
  • Predict Cell Types
  • Train Cell Type Classifier
    • Features used for training and prediction
    • Interpreting the output of Train Cell Type Classifier
    • SVMs for cell type classification
  • Cluster Single Cell Data
    • The Cluster Single Cell Data algorithm
  • Create Heat Map for Cell Abundance
    • Interpreting the output of Create Heat Map for Cell Abundance
• Dimensionality reduction
  • Create UMAP Plot
  • Create tSNE Plot
  • Add Information to Plot
• Expression Analysis
  • Differential Expression for Single Cell
    • Interpreting the output of Differential Expression for Single Cell
    • The differential expression algorithm
  • Create Expression Plot
    • The Heat Map output of Create Expression Plot
    • The Dot Plot output of Create Expression Plot
    • The Violin Plot output of Create Expression Plot
• Immune Repertoire
  • Single Cell Immune Repertoire Analysis
    • The report output from Single Cell Immune Repertoire Analysis
    • The Cell Clonotypes element
    • The clonotype identification algorithm
  • Filter Cell Clonotypes
  • Combine Cell Clonotypes
  • Compare Single Cell Immune Repertoires
    • Interpreting the output of Compare Single Cell Immune Repertoires
  • Convert Clonotypes to Cell Annotations
• Single cell workflows from reads
  • Expression Analysis from Reads
    • Configuring the batch units
    • Output from Expression Analysis from Reads
  • Immune Repertoire Analysis from Reads (10xVDJ)
    • Output from Immune Repertoire Analysis from Reads (10xVDJ)
  • Immune Repertoire and Expression Analysis from Reads (10xVDJ)
    • Configuring the batch units with multiple input types
    • Output from Immune Repertoire and Expression Analysis from Reads (10xVDJ)
• Single cell workflows from imported data
  • Expression Analysis from Matrix
    • Output from Expression Analysis from Matrix
  • Immune Repertoire and Expression Analysis from Clonotypes and Matrix
    • Output from Immune Repertoire and Expression Analysis from Clonotypes and Matrix
• UMAP and tSNE plot functionality
  • Manual Annotation
  • Create Subset
  • Extract to Table
  • Launching of Create Expression Plot
  • Launching of Create Heat Map for Cell Abundance
  • Launching of Differential Expression for Single Cell
• Licensing requirements for the CLC Single Cell Analysis Module
  • Licensing modules on a Workbench
    • Request an evaluation license
    • Download a license using a license order ID
    • Import a license from a file
    • Configure License Server connection
    • Download a static license on a non-networked computer
  • Licensing Server Extensions on a CLC Server
    • Download a static license on a non-networked machine
• Bibliography

The Dot Plot output of Create Expression Plot

A Dot Plot summarizes the expression of all the cells in a grouping for each gene (see figure 9.3). You may need to scroll downwards or to the right to view all the data in the plot. Alternatively Export Graphics can be used to export the entire plot in an image format such as png, for more details see https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Export_graphics_files.html.

In a Dot Plot, the expression values are z-score normalized like they are in the Heat Map. This allows the relative expression of genes with very different average expressions to be visualized in the same plot, but means that expression values cannot be compared between genes - only between cells for the same gene.

Figure 9.3: A Dot Plot visualization of data from [MacParland et al., 2018].

Each combination of gene and cell grouping is represented by a circle whose diameter is proportional to the percentage of cells in the grouping that express that gene. Note that scaling by diameter means that a gene expressed in 50% of cells will have one quarter the area of a gene expressed in 100% of cells. Good marker genes will typically be present in a large percentage of the cells for a cell type.

From the Side Panel, it is possible to change the values by which cells are colored. The options are:

Average expression

The average expression of cells with at least some expression of the gene.

Median expression

The median expression of cells with at least some expression of the gene. The median is more robust than the average in the sense that its value is less affected by outliers.

Variance of expression

The sample variance of cells with at least some expression of the gene. In some cases, when a grouping of cells has high variance for a gene, then this may be evidence that the grouping contains more than one population of cells. Otherwise it may indicate that the gene's expression changes rapidly.

The order in which the clusters or genes are arranged is adjustable in the Features and Groups selection. Click the green plus and use the arrows to remove, add or rearrange the order of the visualized genes or clusters. The coloring can also be changed by clicking the color gradient in the Side Panel. The relative coloring of the values can be adjusted by dragging the two knobs on the white slider above the color gradient.

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